Effects of Geometry, Temperature, and Test Procedure on Reported Failure Strains From Simulated Wide Plate Tests
Bibliographic record
Abstract
Wide plate test is a valuable tool in the assessment of pipeline girth weld integrity. It has been used for welding procedure qualification and for the validation of theoretically based defect assessment procedures. Although the general form of the test has remained largely unchanged over the years, the size of the test specimen, strain measurement, and test procedure, has had some variations. The influence of these variables has not been adequately examined. While this might be acceptable for tests targeted for stress-based design in which a general pass/no-pass answer is desired, the requirements for data accuracy and consistency for strain-based design are much higher. Understanding the variability of the test data is critical for high strain applications. This paper examines the effects of test geometry, mainly the length to width ratio, on the reported failure strains, assuming material’s failure process remains the same. The influence of different strain measdurement procedures, such as the location and gage length of LVDTs (Linear Variable Displacement Transducer), is assessed for different materials and weld strength mismatch levels. The other consideration is the influence of temperature fields on the cold test data. The postulated cold tests use either local cooling at the location of the weld defect or uniform cooling. In the case of local cooling, the gage length of the LVDTs covers materials of different temperatures. Consequently the reported failure strains are affected by the distribution of the temperature fields. The effects of the temperature fields on the reported tensile failure strains are examined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".